Though many try to create methods for enabling a computer to accurately determine the foreground of an image, a method that performs such a task is elusive. There have been a few that have come up with solutions (See e.g., Yu and Shi, “Object-Specific Figure-Ground Segmentation”, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings, Volume 2, pages 39-45, which is hereby incorporated by reference herein in its entirety), but those solutions aren't broad enough to solve the general problem of creating a system or method which would run effectively on any image. Even with advancements in artificial intelligence, satisfactory solutions for having a computer automatically determine the “figure” and “ground,” according to the definitions in psychology literature or as defined by Gestalt rules of perception, remain undiscovered. The application of encoding human perception into machine readable code has proved difficult.
One method for having a computer represent its results for determining the foreground of an image is to direct the computer to segment out the foreground from an image. With the advancement and cost effectiveness of digital photography, many more digital images are being created than ever before. Many of these newly created digital images are taken of a person or people, whereby the person or people are arguably in the foreground of the image. Person or people segmentation from an entire image is currently a popular research topic in the field of computer vision.
Most of the segmentation approaches rely heavily on training sets and accuracy of probabilistic models. Such approaches have the drawback of being computationally demanding and memory intensive. They are also sensitive to model mismatch since they are based heavily on assumptions. Some examples of model based approaches are: (1) “Efficient matching of pictorial structures,” P. F. Felzenszwalb, D. P. Huttenlocher, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 66-73, 2000; (2) “Probabilistic methods for finding people,” S. Ioffe, D. A. Forsyth, International Journal of Computer Vision, vol. 43, issue 1, pp. 45-68, 2001; (3) “Simultaneous detection and segmentation of pedestrians using top-down and bottom-up processing,” V. Sharma, J. W. Davis, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, June 2007; (4) “Bottom up recognition and parsing of the human body,” P. Srinivasan, J. Shi, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, June 2007; and (5) “Detecting and segmenting humans in crowded scenes,” M. D. Rodriguez, M. Shah, Proceedings of the 15th International Conference on Multimedia, pp. 353-356, 2007. Some segmentation approaches rely on rule based systems. Such systems are more forgiving to assumption mismatches than model-based systems. An example of a rule based approach is proposed in Patent Cooperation Treaty Patent Application No. PCT/US2008/013674 entitled “Systems and Methods for Rule-Based Segmentation for Vertical Person or People with Full or Partial Frontal View in Color Images,” filed Dec. 12, 2008.
However, neither approach successfully segments an image of a person within an image whose clothing and background are similar in color. The systems and methods disclosed in the descriptions below provide solutions for segmentation by removal of a monochromatic background with limited intensity variations.